{"id":"W4389121274","doi":"10.1007/978-3-031-42413-7","title":"Bayesian Statistics, New Generations New Approaches","year":2023,"lang":"en","type":"book","venue":"Springer proceedings in mathematics & statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Bayesian probability; Approximate Bayesian computation; Bayesian statistics; Computer science; Variable-order Bayesian network; Parametric statistics; Computation; Econometrics; Bayesian inference; Statistics; Machine learning; Artificial intelligence; Mathematics; Algorithm; Inference","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008396287,0.001476314,0.002232608,0.004075635,0.000735276,0.005431632,0.001805692,0.002684575,0.01109527],"category_scores_gemma":[0.02717887,0.00149462,0.00114246,0.004396417,0.006875164,0.008588945,0.002509181,0.007614704,0.005321549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002560453,"about_ca_system_score_gemma":0.002142075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002872341,"about_ca_topic_score_gemma":0.003934895,"domain_scores_codex":[0.995082,0.002140905,0.0002435393,0.0005794442,0.00184822,0.0001058105],"domain_scores_gemma":[0.9857195,0.01092077,0.0003280975,0.00118519,0.001554841,0.000291629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002428771,0.00002817574,0.0002074837,0.0002936178,0.00006528989,0.00003849184,0.0001845743,0.002219123,0.0001787591,0.8115067,0.05691162,0.1283419],"study_design_scores_gemma":[0.000008136832,0.00000802003,0.0001485142,0.0001247617,0.00001640414,0.00007647722,0.0000398876,0.004006621,0.00005469798,0.8821771,0.1133195,0.00001988747],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.001577046,0.2545578,0.6231201,0.02975862,0.009406549,0.00004500281,0.0005814665,0.0005836343,0.08036978],"genre_scores_gemma":[0.0776682,0.2499575,0.4813222,0.01915854,0.03365816,0.0003596685,0.001150499,0.002041749,0.1346834],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01109527,"threshold_uncertainty_score":0.04440433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1245384737097388,"score_gpt":0.341471544811064,"score_spread":0.2169330711013252,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}